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opensecura / 3p / openxla / iree / 26f63c1a0a85e56c1d395e8668f8abefd27b3643
commit26f63c1a0a85e56c1d395e8668f8abefd27b3643[log] [tgz]
authorZhewen Yu <zhewenyu@amd.com>Fri Aug 29 09:44:50 2025 +0100
committerGitHub <noreply@github.com>Fri Aug 29 09:44:50 2025 +0100
tree0ad812474c0f8a156d07eabd42902572f14bc426
parentb7341d9f7d39b6248389f93cd1e696659adb21f6 [diff]
[GPU][DT] Fix LHS operand offset calculation for DataTiledMMAAttr (#21808)

`DistributeInnerTiledToLanesPass` creates `AffineDelinearizeIndexOp` to
delinearize the thread index within a workgroup and generate offsets for
slicing the operands of InnerTiled operations.

Currently, the delinearization for `DataTiledMMAAttr` relies solely on
the operand swizzle. However, for the LHS, this swizzle does not include
the subGroupsN dimension, which could result in incorrect offset
calculations.

This PR fixes the issue by inserting a dummy subGroupsN dimension when
delinearizing for LHS, ensuring that offsets are generated correctly
according to the full subGroupsM × subGroupsN thread space.

Note that, no special handling is required for the RHS operand, since
subGroupsM is treated as an implicit leading dimension and therefore
omitted anyway.

Closes #21789

Signed-off-by: Yu-Zhewen <zhewenyu@amd.com>
2 files changed
tree: 0ad812474c0f8a156d07eabd42902572f14bc426
  1. .github/
  2. build_tools/
  3. compiler/
  4. docs/
  5. experimental/
  6. integrations/
  7. lib/
  8. llvm-external-projects/
  9. runtime/
  10. samples/
  11. tests/
  12. third_party/
  13. tools/
  14. .bazel_to_cmake.cfg.py
  15. .bazelignore
  16. .bazelrc
  17. .bazelversion
  18. .clang-format
  19. .git-blame-ignore-revs
  20. .gitattributes
  21. .gitignore
  22. .gitmodules
  23. .pre-commit-config.yaml
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. MAINTAINERS.md
  33. README.md
  34. RELEASING.md
  35. WORKSPACE
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.

See our website for project details, user guides, and instructions on building from source.

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Releases notes are published on GitHub releases.

PackageRelease status
GitHub release (stable)GitHub Release
GitHub release (nightly)GitHub Release
iree-base-compilerPyPI version
iree-base-runtimePyPI version

For more details on the release process, see https://iree.dev/developers/general/release-management/.

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Operating systemBuild status
LinuxCI - Linux arm64 clang
macOSCI - macOS x64 clang
macOSCI - macOS arm64 clang

For the full list of workflows see https://iree.dev/developers/general/github-actions/.

Communication channels

Related project channels

  • MLIR topic within LLVM Discourse: IREE is enabled by and heavily relies on MLIR. IREE sometimes is referred to in certain MLIR discussions. Useful if you are also interested in MLIR evolution.

Architecture overview

IREE Architecture IREE Architecture

See our website for more information.

Presentations and talks

Community meeting recordings: IREE YouTube channel

DateTitleRecordingSlides
2025-06-10Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM)recordingslides
2025-05-17Introduction to GPU architecture and IREE's GPU CodeGen Pipelinerecordingslides
2025-02-12The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised)recordingslides
2024-10-01Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardwarerecording
2021-06-09IREE Runtime Design Tech Talkrecordingslides
2020-08-20IREE CodeGen (MLIR Open Design Meeting)recordingslides
2020-03-18Interactive HAL IR Walkthroughrecording
2020-01-31End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting)recordingslides

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.